Neural network approaches to some model flow control problems

“…Our work is motivated by the possibility of a simple control strategy using NN models directly in the Wichita, Kansas 67260 model-based predictive control system structure. The proposed NN model is a recurrent type NN trained by dynamic sequential recursive backpropagation learning algorithm (DSRB) [3]. The on-line DSRB based learning with continuous correction is effectively applicable to realize the dynamic functional relationships in a system with changing parameters or uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control of aircraft dynamics using neural networks

Nho

Agarwal²

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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In this paper a neural network model-based predictive control strategy for aircraft systems with unknown parameters is presented. The objective of the paper is to stabilize unknown systems by the adaptive control law through an optimization procedure in which a cost function representing the deviation error between set-points and the predicted outputs obtained from a neural network is minimized Due to its capability of characterizing dynamic functional relationships and its feedback processing structure, a recurrent neural network is employed as an adaptive estimator for future state values. The neural network training is performed by the dynamic sequential recursive backpropagation learning algorithm, which allows the neural network to be trained on-line. It is shown that the proposed neural network learning algorithm has potential for designing flight control systems which can compensate for unpredictable changes in an aircraft dynamics over a wide range of flight conditions and other uncertainties. 0-7803-5529-6/99/$10.00 01999 IEEE

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Reduced Order Nonlinear Navier-Stokes Models for Synthetic Jets

Rediniotis

Kurdila

2002

Journal of Fluids Engineering

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While the potential for the use of synthetic jet actuators to achieve flow control has been noted for some time, most of such flow control studies have been empirical or experimental in nature. Several technical issues must be resolved to achieve rigorous, model-based, closed-loop control methodologies for this class of actuators. The goal of this paper is consequently two-fold. First, we seek to derive and evaluate model order reduction methods based on proper orthogonal decomposition that are suitable for synthetic jet actuators. Second, we seek to derive rigorously stable feedback control laws for the derived reduced order models. The realizability of the control strategies is discussed, and a numerical study of the effectiveness of the reduced order models for two-dimensional flow near the jet exit is summarized.

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Neural network approaches to some model flow control problems

Cited by 4 publications

References 5 publications

Neural Network Modeling for Near Wall Turbulent Flow

Neural Network Modeling for Near Wall Turbulent Flow

Adaptive control of aircraft dynamics using neural networks

Reduced Order Nonlinear Navier-Stokes Models for Synthetic Jets

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